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Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology / Aura Salmivaara in Forestry, an international journal of forest research, vol 93 n° 5 (October 2020)
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Titre : Towards dynamic forest trafficability prediction using open spatial data, hydrological modelling and sensor technology Type de document : Article/Communication Auteurs : Aura Salmivaara, Auteur ; Samuli Launiainen, Auteur ; Jari Perttunen, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 662 - 674 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Environnement
[Termes IGN] apprentissage automatique
[Termes IGN] chemin forestier
[Termes IGN] classification barycentrique
[Termes IGN] dégradation des sols
[Termes IGN] dommage
[Termes IGN] données localisées libres
[Termes IGN] exploitation forestière
[Termes IGN] Finlande
[Termes IGN] humidité du sol
[Termes IGN] modèle dynamique
[Termes IGN] modèle hydrographiqueRésumé : (auteur) Forest harvesting operations with heavy machinery can lead to significant soil rutting. Risks of rutting depend on the soil bearing capacity which has considerable spatial and temporal variability. Trafficability prediction is required in the selection of suitable operation sites for a given time window and conditions, and for on-site route optimization during the operation. Integrative tools are necessary to plan and carry out forest operations with minimal negative ecological and economic impacts. This study demonstrates a trafficability prediction framework that utilizes a spatial hydrological model and a wide range of spatial data. Trafficability was approached by producing a rut depth prediction map at a 16 × 16 m grid resolution, based on the outputs of a general linear mixed model developed using field data from Southern Finland, modelled daily soil moisture, spatial forest inventory and topography data, along with field measured rolling resistance and information on the mass transported through the grid cells. Dynamic rut depth prediction maps were produced by accounting for changing weather conditions through hydrological modelling. We also demonstrated a generalization of the rolling resistance coefficient, measured with harvester CAN-bus channel data. Future steps towards a nationwide prediction framework based on continuous data flow, process-based modelling and machine learning are discussed. Numéro de notice : A2020-790 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1093/forestry/cpaa010 Date de publication en ligne : 05/10/2020 En ligne : https://doi.org/10.1093/forestry/cpaa010 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96559
in Forestry, an international journal of forest research > vol 93 n° 5 (October 2020) . - pp 662 - 674[article]Urban Wi-Fi fingerprinting along a public transport route / Guenther Retscher in Journal of applied geodesy, vol 14 n° 4 (October 2020)
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Titre : Urban Wi-Fi fingerprinting along a public transport route Type de document : Article/Communication Auteurs : Guenther Retscher, Auteur ; Aizhan Bekenova, Auteur Année de publication : 2020 Article en page(s) : pp 379 – 392 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] accès sans fils à l'internet
[Termes IGN] cartographie et localisation simultanées
[Termes IGN] centrale inertielle
[Termes IGN] empreinte
[Termes IGN] itinéraire
[Termes IGN] migration pendulaire
[Termes IGN] positionnement par WiFi
[Termes IGN] programmation par contraintes
[Termes IGN] qualité du signal
[Termes IGN] service fondé sur la position
[Termes IGN] téléphone intelligent
[Termes IGN] transport collectif
[Termes IGN] zone urbaineRésumé : (auteur) The outreach of Wi-Fi localization is extended in this study for urban wide applications as they provide the high potential to employ them for numerous applications for localization and guidance in urban environments. The selected application presented in this paper is the localization and routing of public transport smartphone users. For the conducted investigations, Received Signal Strength Indicator (RSSI) values are collected for users who are travelling from home in a residential neighbourhood to work in the city centre and return along the same route. Special tramway trains are selected which provide two on-board Wi-Fi Access Points (APs). Firstly, the availability, visibility and RSSI stability of the Wi-Fi signal behavior of these APs and the APs in the surrounding environment along the routes is analyzed. Then the trajectories are estimated based on location fingerprinting. A first analyses reveals that significant differences exists between the six employed smartphones as well as times of the day, e. g. in the morning at peak hours or at off-peak hours. From the long-time observations it is seen that the two on-board APs show a high stability of the RSSI signals at the same times of the day and along the whole route. It is therefore currently investigated how they can confirm and validate user localization along the route and if they can contribute to constrain the overall positioning solution in combination with the inertial smartphone sensors. Moreover, the railway track can serve as a further constraint. As an outlook on future work, the development of a Simultaneous Localization and Mapping (SLAM) solution with a fusion with the smartphone inertial sensors is proposed. Numéro de notice : A2020-676 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2020-0015 Date de publication en ligne : 16/07/2020 En ligne : https://doi.org/10.1515/jag-2020-0015 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96168
in Journal of applied geodesy > vol 14 n° 4 (October 2020) . - pp 379 – 392[article]An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering / Xiaojing Wu in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)
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Titre : An overview of clustering methods for geo-referenced time series: from one-way clustering to co- and tri-clustering Type de document : Article/Communication Auteurs : Xiaojing Wu, Auteur ; Changxiu Cheng, Auteur ; Raul Zurita-Milla, Auteur Année de publication : 2020 Article en page(s) : pp 1822 - 1848 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] analyse spatio-temporelle
[Termes IGN] classification barycentrique
[Termes IGN] classification par nuées dynamiques
[Termes IGN] exploration de données
[Termes IGN] géoréférencement
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] regroupement de données
[Termes IGN] série temporelle
[Termes IGN] taxinomieRésumé : (auteur) Even though many studies have shown the usefulness of clustering for the exploration of spatio-temporal patterns, until now there is no systematic description of clustering methods for geo-referenced time series (GTS) classified as one-way clustering, co-clustering and tri-clustering methods. Moreover, the selection of a suitable clustering method for a given dataset and task remains to be a challenge. Therefore, we present an overview of existing clustering methods for GTS, using the aforementioned classification, and compare different methods to provide suggestions for the selection of appropriate methods. For this purpose, we define a taxonomy of clustering-related geographical questions and compare the clustering methods by using representative algorithms and a case study dataset. Our results indicate that tri-clustering methods are more powerful in exploring complex patterns at the cost of additional computational effort, whereas one-way clustering and co-clustering methods yield less complex patterns and require less running time. However, the selection of the most suitable method should depend on the data type, research questions, computational complexity, and the availability of the methods. Finally, the described classification can include novel clustering methods, thereby enabling the exploration of more complex spatio-temporal patterns. Numéro de notice : A2020-477 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1726922 Date de publication en ligne : 16/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1726922 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95624
in International journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1822 - 1848[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2020091 RAB Revue Centre de documentation En réserve L003 Disponible Applying multi-temporal Landsat satellite data and Markov-cellular automata to predict forest cover change and forest degradation of sundarban reserve forest, Bangladesh / Mohammad Emran Hasan in Forests, vol 11 n° 9 (September 2020)
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Titre : Applying multi-temporal Landsat satellite data and Markov-cellular automata to predict forest cover change and forest degradation of sundarban reserve forest, Bangladesh Type de document : Article/Communication Auteurs : Mohammad Emran Hasan, Auteur ; Biswajit Nath, Auteur ; A.H.M. Raihan Sarker, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : N° 1016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] automate cellulaire
[Termes IGN] Bangladesh
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] couvert forestier
[Termes IGN] déboisement
[Termes IGN] dégradation de l'environnement
[Termes IGN] détection de changement
[Termes IGN] gestion forestière durable
[Termes IGN] image Landsat-OLI
[Termes IGN] image Landsat-TM
[Termes IGN] mangrove
[Termes IGN] modèle de Markov
[Termes IGN] modèle de simulation
[Termes IGN] occupation du sol
[Termes IGN] réserve forestière
[Termes IGN] réserve naturelle
[Termes IGN] santé des forêts
[Termes IGN] série temporelle
[Termes IGN] système d'information géographiqueRésumé : (auteur) Overdependence on and exploitation of forest resources have significantly transformed the natural reserve forest of Sundarban, which shares the largest mangrove territory in the world, into a great degradation status. By observing these, a most pressing concern is how much degradation occurred in the past, and what will be the scenarios in the future if they continue? To confirm the degradation status in the past decades and reveal the future trend, we took Sundarban Reserve Forest (SRF) as an example, and used satellite Earth observation historical Landsat imagery between 1989 and 2019 as existing data and primary data. Moreover, a geographic information system model was considered to estimate land cover (LC) change and spatial health quality of the SRF from 1989 to 2029 based on the large and small tree categories. The maximum likelihood classifier (MLC) technique was employed to classify the historical images with five different LC types, which were further considered for future projection (2029) including trends based on 2019 simulation results from 1989 and 2019 LC maps using the Markov-cellular automata model. The overall accuracy achieved was 82.30%~90.49% with a kappa value of 0.75~0.87. The historical result showed forest degradation in the past (1989–2019) of 4773.02 ha yr−1, considered as great forest degradation (GFD) and showed a declining status when moving with the projection (2019–2029) of 1508.53 ha yr−1 and overall there was a decline of 3956.90 ha yr−1 in the 1989–2029 time period. Moreover, the study also observed that dense forest was gradually degraded (good to bad) but, conversely, light forest was enhanced, which will continue in the future even to 2029 if no effective management is carried out. Therefore, by observing the GFD, through spatial forest health quality and forest degradation mapping and assessment, the study suggests a few policies that require the immediate attention of forest policy-makers to implement them immediately and ensure sustainable development in the SRF. Numéro de notice : A2020-752 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f11091016 Date de publication en ligne : 21/09/2020 En ligne : https://doi.org/10.3390/f11091016 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96432
in Forests > vol 11 n° 9 (September 2020) . - N° 1016[article]L-band SAR for estimating aboveground biomass of rubber plantation in Java Island, Indonesia / Bambang H Trisasongko in Geocarto international, vol 35 n° 12 ([01/09/2020])
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Titre : L-band SAR for estimating aboveground biomass of rubber plantation in Java Island, Indonesia Type de document : Article/Communication Auteurs : Bambang H Trisasongko, Auteur ; David J. Paull, Auteur Année de publication : 2020 Article en page(s) : pp 1327 - 1342 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage automatique
[Termes IGN] arbre hors forêt
[Termes IGN] bande L
[Termes IGN] bilan du carbone
[Termes IGN] biomasse aérienne
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données allométriques
[Termes IGN] données polarimétriques
[Termes IGN] hevea (genre)
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image radar moirée
[Termes IGN] Java (île de)Résumé : (auteur) This article discusses an experiment on the estimation of rubber tree biomass using L-band Synthetic Aperture Radar (SAR), to support recent efforts to include trees outside forest in global biomass and carbon accounting. We noted that date of acquisition is important, but certainly the selection of allometric equation serving as the reference data was paramount. Similarly, choosing a proper form of fully polarimetric data was instrumental, although this requires validation in different environmental settings. As expected, modern data mining approaches consistently delivered high accuracy. Extreme learning machine yielded the best estimate in terms of R2 (0.98) and RMSE (1.88 Mg/ha); nonetheless, it also delivered a slight negative estimation. In this case, we found that a variant of random forest produced an outcome without any negative estimation. This research suggests that estimated biomass or carbon information from rubber plantations would be an invaluable candidate for the improvement of global biomass data. Numéro de notice : A2020-480 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1573855 Date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1573855 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95630
in Geocarto international > vol 35 n° 12 [01/09/2020] . - pp 1327 - 1342[article]Comparing pedestrians’ gaze behavior in desktop and in real environments / Weihua Dong in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)
PermalinkCrater detection and registration of planetary images through marked point processes, multiscale decomposition, and region-based analysis / David Solarna in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)
PermalinkCSVM architectures for pixel-wise object detection in high-resolution remote sensing images / Youyou Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)
PermalinkGeovisualization and harmonic analysis for the exploratory search of localized cyclic recurrences in spatio-temporal event data / Jacques Gautier in Geomatica, vol 74 n° 3 (September 2020)
PermalinkHeliport detection using artificial neural networks / Emre Baseski in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)
PermalinkA lightweight ensemble spatiotemporal interpolation model for geospatial data / Shifen Cheng in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)
PermalinkMapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine / Aparna R. Phalke in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)
PermalinkMultiscale supervised kernel dictionary learning for SAR target recognition / Lei Tao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)
PermalinkNEAT approach for testing and validation of geospatial network agent-based model processes: case study of influenza spread / Taylor Anderson in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)
PermalinkA novel deep learning instance segmentation model for automated marine oil spill detection / Shamsudeen Temitope Yekeen in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)
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